The onset of most human disease involves multiple, molecular-level changes to the complex system of interacting genes and pathways that function differently in specific cell-lineage, pathway and treatment contexts. While this system has been probed by the thousands of functional genomics and quantitative genetic studies, careful extraction of signals relevant to these specific contexts is a challenging problem. General integration of these heterogeneous data was an important first step in detecting signals that be used to build networks to generate experimentally-testable hypotheses. However, only by dealing with the fact that disease happens at the intersection of multiple contexts and by integrating functional genomics with quantitative genetics will we be able to move toward a molecular-level understanding of human pathophysiology, which will pave the way to new therapy and drug development. The long-term goal of this project is to enable such discoveries through the development of innovative bioinformatics frameworks for integrative analysis of diverse functional genomic data. In the previous funding periods, we developed accurate data integration and visualization methodologies for most common model organisms and human, created methods for tissue-specific data analysis, and applied these methods to make novel insights about important biological processes. We further enabled experimental biological discovery by implementing these methods in publicly accessible interactive systems that are popular with experimental biologists. Leveraging our prior work, we now will directly address the challenge of enabling data-driven study of molecular mechanisms underlying human disease by developing novel semi-supervised and multi-task machine learning approaches and implementing them in a real-time integration system capable of predicting genome-scale functional and mechanism-specific networks focused on any biological context of interest. This will allow any biomedical researcher to quickly make data-driven hypotheses about function, interactions, and regulation of genes involved in hypertension in the kidney glomerulus or to predict new regulatory interactions relevant to Parkinson's disease that affect the ubiquitination pathway in Substantia nigra. Furthermore, we will develop methods for disease gene discovery that leverage these highly specific networks for functional analysis of quantitative genetics data. Our deliverable will be a general, robust, user-friendly, and automatically updated system for user-driven functional genomic data integration and functional analysis of quantitative genetics data. Throughout this work, we (with our close experimental and clinical collaborators) will also apply our methods to chronic kidney disease, cardiovascular disease/hypertension, and autism spectrum disorders both as case studies for the iterative improvement of our methods and to make direct contribution to better understanding of these diseases.